Cumulative versus Gravity-based Accessibility Measures: Which One to Use?

There is a lack of agreement regarding the theoretical framework that practitioners should use for accessibility assessment – a measure of transport and land-use systems performance. Cumulative measures are simple and easy to interpret, while gravity-based measures are more sophisticated, resourceful, and less intuitive approaches. As such, this study aims to investigate whether the estimates of a simple cumulative opportunity measure are significantly different from those made using advanced gravity-based measures to understand if the former can be a substitute for the latter in practice and if a certain threshold of travel time can be recommended for different regions. We estimated cumulative and gravity-based accessibility using decay-probability density functions, decay-cumulative density functions, Gaussian, and a Log-Logistic decay-cumulative density functions using census commuting flows, car congested travel time and public transit schedules from eight metropolitan regions across Canada – Toronto, Montreal, Vancouver, Edmonton, Quebec City, Winnipeg, London, and Halifax. These measures of accessibility were tested for correlation, and we found that a coefficient of approximately 0.90 is reached when the threshold to calculate cumulative opportunities accessibility is set to the average commute time for both low- and non-low-wage jobs accessibility analyses by transit and motor vehicles. The paper provides evidence to support the reliability of cumulative accessibility, facilitates its broader adoption for evaluation of transport and land use interactions in North American cities, as well as opens opportunities to advance the equitable distribution of transport system benefits.

Commute trips typically constitute a major share of weekday trips made in urban areas. Hence, commute distances and modes of travel usage are closely linked with the level of transport sustainability of a city. The COVID-19 pandemic has resulted in a significant change in commute patterns – the length and frequency of commute trips and the usage rate of public transport in particular. To ensure the long-term sustainability of transport in a world faced with the persistent threat of potential pandemics, it is crucial to understand these changes. This paper empirically examines how the residential locations and commute patterns of a segment of subway commuters in Beijing changed during the peak of the COVID-19 pandemic. Passively generated smart card data from 8,792,539 subway users were used to quantify the relative impact of different factors contributing to these changes. The results indicate a notable trend of residential relocation towards the city centre that is opposite to the trend of moving away from city centres observed in some other countries. Further, it is observed that the pandemic has acted as a catalyst for individuals with long commute times (over 45 min) to reconsider their locations, aiming to reduce commuting time. Consequently, such relocations lead to an average commute time reduction. The findings contribute to advancing existing knowledge related to the long-term mobility and sustainability implications of the pandemic, some of which are expected to be transferable to future pandemic contexts.

Traditional public transportation mostly uses fixed schedules to meet society’s needs reliably. Alternatively, one can design a dynamic policy to update the schedule according to the state of the network. Using such policy, any dynamic changes in demand, travel times, traffic, geometry, or emergency modes will be reflected in the real-time scheduling. In this study, the application of reinforcement learning for public transportation scheduling and routing is proposed considering electric vehicles. Reinforcement learning is a right choice for online decision-making in a time-variant setting. Reflecting electric vehicles’ characteristics on public transportation scheduling supports the necessity of considering a dynamic scheduling policy, such as vehicle-to-grid transaction capability or dynamic charging strategies. The proposed scheduling methodology is evaluated for a customary six-stop network. The trained agent takes actions based on the number of waiting people, the drop-off requests, battery level, time, and the current location. The assumed models for the traffic load effect, the passenger flow per stops, and the electricity price add nonlinearity to the problem in which this algorithm will behave more conveniently compared to a fixed schedule setting. Using this method, the electric bus can choose the next destination, the optimal driving speed for each interval, and the amount of electricity to charge or to sell for optimal financial gain when connected to the charging infrastructure. The proposed environment is built numerically, and a learning-based agent is trained for this purpose. Our study supports that the optimal dynamic agent outperforms a fixed schedule policy in different modes of network performance, particularly, when the network undergoes abrupt demand changes.KeywordsReinforcement LearningRoutingPublic transitOptimizationSAC (soft actor-critic)Electric vehicle

Scheduled public transport (PT) service on a defined network often encounters unforeseen variations of arrival times mainly because of traffic problems, unexpected passenger demand, and driver behavior. These variations will create the undesirable vehicle (particularly bus) bunching phenomenon unless a proper control action is introduced. This study develops a methodology to attain optimally real-time control actions to minimize the bunching phenomenon. To this end, a “library” of selected operational tactics is constructed, for the PT operators, not only to assist in reducing vehicle bunching but also to increase the likelihood of direct (without await) transfers. The library of operational tactics serves as a basis for a process of real-time control actions to maintain the scheduled headway and thus achieving maximal transfer synchronization. The methodology developed, using simulation, is applied to a case study of actual bus routes operated in the region of Auckland, New Zealand. The results imply what tactic to use in real time, what is the optimal control strength, and what is the saving of riding and waiting times in comparison with PT operation without using tactics. The findings show that a significant improvement, in avoiding bunching and increasing the number of direct transfers, is attained by the use of a combination of selected tactics.

Commute Time and Labor Supply

An investigation of Bus Rapid Transit System (BRT) based on economic and air pollution analysis (Tehran, Iran)

Flexible transport services include a range of passenger transport mobility offers, where services are flexible in one or more of the dimensions of route, vehicle allocation, vehicle operator, type of payment and passenger category. Although flexible transport services are used increasingly in Europe and the USA as part of the public transport mix where regular services are not sustainable, there are few working examples of these services in urban Australia, although there are many opportunities. Through a series of semi-structured interviews with key stakeholders in New South Wales, Australia, the article identifies barriers to greater use of flexible transport services in low-density urban areas. These five sets of barriers include: institutional frameworks such as regulation; economic issues of funding and cost; operational issues such as fleet and vehicles; operator and community attitudes, awareness and cultures; and information and education. The article makes recommendations to enable and encourage greater use of flexible transport services by transport service planners and providers as part of the public transport mix in low-density urban environments where it can be more difficult to provide regular and scheduled public transport.

Schedule optimization is a proven strategy to improve service quality for public transport networks. However, current research mostly optimizes schedule design using prior knowledge of users’ routings, ignoring the optimization of public transport schedules on duplicating stretches of route vehicles. This article presents a new alignment technique of time intervals between consecutive vehicles of different routes on duplicating stretches, considering existing public transport networks in attaining optimization of public transport schedule. Scheduling technique of route vehicles on duplicating stretches includes some steps: analysis of public transport network and determining a lot of duplicating stretches, calculation of the optimal time intervals among arrivals of route vehicles and alignment of these intervals among consecutive route vehicles on duplicating stretches, realization simulation model of urban passenger transport within the simulation modeling system of GPSS World. Furthermore, testing the optimization technique of route vehicle scheduling, analysis of the quality of adjusted schedule with route vehicles of different kinds included, and determining the optimization efficiency for duplicating stretches. The adjusting technique of urban passenger transport schedule allows to increase movement steadiness of consecutive vehicles of different routes on duplicating stretches, adjust traffic intervals for each route, shorten the traffic load on stations, reducing idle time and queue lengths of route vehicles in front of transport stops and also minimize waiting time for route vehicle by those passengers who can be transported along several routes. Improving the bus schedule on duplicating stretches in Gomel is illustrated demonstrating the technique developed. This scheduling technique can be used by carriers and transport operators to improve the quality of services provided.

A random utility maximization (RUM) based measure of accessibility to transit: Accurate capturing of the first-mile issue in urban transit

Introduction Many workers struggle to achieve enough sleep due to demands of work, caregiving, and other commitments. Prior research has found that increased commute time is associated with decreased sleep, with the assumption that this is largely due to the time burden. However, the mode of commute – driving, carpooling, mass transit, biking, or walking – likely differ in terms of stress, physical activity, ability to multi-task, scheduling flexibility, etc., which may affect sleep as well. This study explores the relationship between commute time, transportation mode, and their impact on sleep duration. Methods This study utilizes data from the 2021 to 2023 American Time Use Survey (ATUS), which includes 24-hour time use diaries on a representative sample of adults in the United States. This analysis was restricted to those with at least two instances of commuting to or from work, and excluding those with airplane as a transportation mode. We categorized time use into five groups: commute, sleep, work, leisure, and non-discretionary activities (eating, household chores, caregiving, etc.), and used linear regression to examine the association between commute time and sleep duration, adjusting for potential confounders (work schedule, age, sex, and rural/urban location.) Results Each one minute increase in commute times was associated with a decrease in sleep time during the same 24-hour period, including 0.32 minutes (95%CI 0.24 to 0.40) for each minute of driving, 0.26 minutes (95%CI 0.12 to 0.41) for sedentary non-driving commutes (such as bus, train, and carpooling), and 0.59 (95%CI 0.16 to 1.02) for active commute modes (such as walking or biking). Conclusion Contrary to expectations, the benefits of active commutes did not decrease the impact of commute time on sleep duration. However, we do see a decreased effect of non-driving sedentary commuting vs. driving, and hypothesize that this may be due to the ability to multi-task while a non-driver on a sedentary commute, potentially having a less stressful commute than those driving, and/or needing to stick to a stricter schedule on when to leave work. Further research is needed to better understand these differences, and to translate them into actionable strategies for urban planning and transportation policies. Support (if any)

<p>Access to healthcare services is one of the fundamental pillars of society today. People either have access to healthcare or they are not privileged enough to visit a doctor. In underprivileged societies, access to healthcare is difficult due to various reasons such as fewer number of doctors and healthcare facilities, inefficient communication means, and so on. This may result in exaggeration of the disease or threat to life. On the other hand, those who are privileged, end up paying unnecessary visits to the physician in most of the cases on an average (> 71% in USA), and incurring high costs. Often-times, health issues are minor, and it is not necessary to visit a doctor. In such a case, the patient only requires suggestions on quick health fix, or some precautionary measures. Such information can be provided through the combination of a smartphone-based app, fog/edge computing, and mobile communication. Hence, reaching out to the doctors in their available time (off-time, commuting via public transit, so on) when they can answer some quick questions is one of the solutions that fit into smart healthcare definition, and truly pervasive healthcare. In this paper, we propose an innovative healthcare service architecture, called opportunistic healthcare (oHealth), where health log and questions can be opportunistically offloaded to a nearby (such as at public transit) compute entity (such as fog server) capable of processing health logs (up to 234,729 logs daily as per our setup). The fog node communicates the questions to a doctor, who provides the necessary feedback. We provide proof-of-concept evaluation results (in WiFi vs 4G networks, on the basis of delay, cost, and commute time, comparing cloud with fog) to endorse the applicability of our oHealth. </p>

Much research on human mobility patterns and accessibility to date has been conducted largely in Western European and North American countries, where the private vehicle is the main means for commuting. As a result, most studies focused largely on car‐based mobility (automobility) and accessibility, and relatively little is known about countries in other regions of the world. Based on an activity‐travel dataset collected in Sofia, Bulgaria and using 3D geovisualisation, this study attempts to fill this gap through examining gender differences in commute time and potential access to urban opportunities. The results reveal important gender differences in commute time and individual accessibility. Among the surveyed participants, women tend to spend more time on their commute trips and have more restrictive spatial reach to urban opportunities compared with men, largely as a result of their reliance on public transit as their primary mode of transport. Further, women have lower accessibility compared with men who used the same travel mode. This case study adds important new knowledge about a geographical area that has been under‐studied by Anglophone geographers. It also shows that GIS‐based geovisualisation and analysis are powerful tools for uncovering gender differences in the geographical distribution of commute time, which conventional quantitative methods cannot reveal.

• Changes in bus routes can have an important impact on accessibility measures. • Need for tools to monitor the impacts of changes in public transport configurations. • The study implements interactive tools to predict changes in accessibility. • Increased availability of open-source solutions has the potential to address such needs. An intuitive and interactive opensource web tool for measuring access to services via public transport is described. The application allows policymakers to analyse and compare the impacts that alternative network configurations may have upon access to services. The application facilitates interactive analyses to support decision-makers in making better informed choices. The study addresses key technical challenges to integrating opensource technologies with bus timetable information. A cumulative opportunity measure of access to essential services is computed under alternative real-world scenarios. These consider the complete withdrawal of an agency, the removal of a route, and addition of a new route. We highlight the need to promote the wider use of such interactive tools amongst planners and transport professionals when examining access to services via public transport.

Public transportation schedules are designed by agencies to optimize service quality under multiple constraints. However, real service usually deviates from the plan. Therefore, transportation analysts need to identify, compare and explain both eventual and systemic performance issues that must be addressed so that better timetables can be created. The purely statistical tools commonly used by analysts pose many difficulties due to the large number of attributes at trip- and station-level for planned and real service. Also challenging is the need for models at multiple scales to search for patterns at different times and stations, since analysts do not know exactly where or when relevant patterns might emerge and need to compute statistical summaries for multiple attributes at different granularities. To aid in this analysis, we worked in close collaboration with a transportation expert to design TR-EX, a visual exploration tool developed to identify, inspect and compare spatio-temporal patterns for planned and real transportation service. TR-EX combines two new visual encodings inspired by Marey's Train Schedule: Trips Explorer for trip-level analysis of frequency, deviation and speed; and Stops Explorer for station-level study of delay, wait time, reliability and performance deficiencies such as bunching. To tackle overplotting and to provide a robust representation for a large numbers of trips and stops at multiple scales, the system supports variable kernel bandwidths to achieve the level of detail required by users for different tasks. We justify our design decisions based on specific analysis needs of transportation analysts. We provide anecdotal evidence of the efficacy of TR-EX through a series of case studies that explore NYC subway service, which illustrate how TR-EX can be used to confirm hypotheses and derive new insights through visual exploration.

PurposeThe study aims to propose an intelligent real-time traffic model to address the traffic congestion problem. The proposed model assists the urban population in their everyday lives by assessing the probability of road accidents and accurate traffic information prediction. It also helps in reducing overall carbon dioxide emissions in the environment and assists the urban population in their everyday lives by increasing overall transportation quality.Design/methodology/approachThis study offered a real-time traffic model based on the analysis of numerous sensor data. Real-time traffic prediction systems can identify and visualize current traffic conditions on a particular lane. The proposed model incorporated data from road sensors as well as a variety of other sources. It is difficult to capture and process large amounts of sensor data in real time. Sensor data is consumed by streaming analytics platforms that use big data technologies, which is then processed using a range of deep learning and machine learning techniques.FindingsThe study provided in this paper would fill a gap in the data analytics sector by delivering a more accurate and trustworthy model that uses internet of things sensor data and other data sources. This method can also assist organizations such as transit agencies and public safety departments in making strategic decisions by incorporating it into their platforms.Research limitations/implicationsThe model has a big flaw in that it makes predictions for the period following January 2020 that are not particularly accurate. This, however, is not a flaw in the model; rather, it is a flaw in Covid-19, the global epidemic. The global pandemic has impacted the traffic scenario, resulting in erratic data for the period after February 2020. However, once the circumstance returns to normal, the authors are confident in their model’s ability to produce accurate forecasts.Practical implicationsTo help users choose when to go, this study intended to pinpoint the causes of traffic congestion on the highways in the Bay Area as well as forecast real-time traffic speeds. To determine the best attributes that influence traffic speed in this study, the authors obtained data from the Caltrans performance measurement system (PeMS), reviewed it and used multiple models. The authors developed a model that can forecast traffic speed while accounting for outside variables like weather and incident data, with decent accuracy and generalizability. To assist users in determining traffic congestion at a certain location on a specific day, the forecast method uses a graphical user interface. This user interface has been designed to be readily expanded in the future as the project’s scope and usefulness increase. The authors’ Web-based traffic speed prediction platform is useful for both municipal planners and individual travellers. The authors were able to get excellent results by using five years of data (2015–2019) to train the models and forecast outcomes for 2020 data. The authors’ algorithm produced highly accurate predictions when tested using data from January 2020. The benefits of this model include accurate traffic speed forecasts for California’s four main freeways (Freeway 101, I-680, 880 and 280) for a specific place on a certain date. The scalable model performs better than the vast majority of earlier models created by other scholars in the field. The government would benefit from better planning and execution of new transportation projects if this programme were to be extended across the entire state of California. This initiative could be expanded to include the full state of California, assisting the government in better planning and implementing new transportation projects.Social implicationsTo estimate traffic congestion, the proposed model takes into account a variety of data sources, including weather and incident data. According to traffic congestion statistics, “bottlenecks” account for 40% of traffic congestion, “traffic incidents” account for 25% and “work zones” account for 10% (Traffic Congestion Statistics). As a result, incident data must be considered for analysis. The study uses traffic, weather and event data from the previous five years to estimate traffic congestion in any given area. As a result, the results predicted by the proposed model would be more accurate, and commuters who need to schedule ahead of time for work would benefit greatly.Originality/valueThe proposed work allows the user to choose the optimum time and mode of transportation for them. The underlying idea behind this model is that if a car spends more time on the road, it will cause traffic congestion. The proposed system encourages users to arrive at their location in a short period of time. Congestion is an indicator that public transportation needs to be expanded. The optimum route is compared to other kinds of public transit using this methodology (Greenfield, 2014). If the commute time is comparable to that of private car transportation during peak hours, consumers should take public transportation.